Performance of climate field reconstruction methods over multiple seasons and climate variables

Abstract

Studies of climate variability require long time series of data but are limited by the absence of preindustrial instrumental records. For such studies, proxy‐based climate reconstructions, such as those produced from tree‐ring widths, provide the opportunity to extend climatic records into preindustrial periods. Climate field reconstruction (CFR) methods are capable of producing spatially‐resolved reconstructions of climate fields. We assessed the performance of three commonly used CFR methods (canonical correlation analysis, point‐by‐point regression, and regularized expectation maximization) over spatially‐resolved fields using multiple seasons and climate variables. Warm‐ and cool‐season geopotential height, precipitable water, and surface temperature were tested for each method using tree‐ring chronologies. Spatial patterns of reconstructive skill were found to be generally consistent across each of the methods, but the robustness of the validation metrics varied by CFR method, season, and climate variable. The most robust validation metrics were achieved with geopotential height, the October through March temporal composite, and the Regularized Expectation Maximization method. While our study is limited to assessment of skill over multidecadal (rather than multi‐centennial) time scales, our findings suggest that the climate variable of interest, seasonality, and spatial domain of the target field should be considered when assessing potential CFR methods for real‐world applications.